ArticleScientific reports2024
Multi-omics integration of scRNA-seq time series data predicts new intervention points for Parkinson's disease.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
13 citing papers in PubMed.
- Semi-supervised ensemble learning with interval type-2 fuzzy-rough sets for Parkinson's disease prediction from multi-omics.Mammalian genome : official journal of the International Mammalian Genome Society · 2026Article
- When complexity does not pay: benchmarking deep learning and ensemble methods for biomarker discovery.Briefings in bioinformatics · 2026Article
- Neurosenescence, inflammaging and neuroinflammation in neurodegenerative disorders.Frontiers in aging · 2026Review
- Research progress of single cell RNA sequencing in nervous system.Molecular biology reports · 2025Review
- Multi-omic network inference from time-series data.NPJ systems biology and applications · 2025Article
- How quantum computing can enhance biomarker discovery.Patterns (New York, N.Y.) · 2025Review
- Simplicity within biological complexity.Bioinformatics advances · 2025Article
- Integrative Metabolome and Proteome Analysis of Cerebrospinal Fluid in Parkinson's Disease.International journal of molecular sciences · 2024Review
- MONFIT: multi-omics factorization-based integration of time-series data sheds light on Parkinson's disease.NAR molecular medicine · 2024Article
- M3NetFlow: A novel multi-scale multi-hop graph AI model for integrative multi-omic data analysis.bioRxiv : the preprint server for biology · 2024Article
- Bridging the gap in precision medicine: TranSYS training programme for next-generation scientists.Frontiers in medicine · 2024Article
- Integrative approach of omics and imaging data to discover new insights for understanding brain diseases.Brain communications · 2024Review
- The axes of biology: a novel axes-based network embedding paradigm to decipher the functional mechanisms of the cell.Bioinformatics advances · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Parkinson's disease (PD) is a complex neurodegenerative disorder without a cure. The onset of PD symptoms corresponds to 50% loss of midbrain dopaminergic (mDA) neurons, limiting early-stage understanding of PD. To shed light on early PD development, we study time series scRNA-seq datasets of mDA neurons obtained from patient-derived induced pluripotent stem cell differentiation. We develop a new data integration method based on Non-negative Matrix Tri-Factorization that integrates these datasets with molecular interaction networks, producing condition-specific "gene embeddings". By mining these embeddings, we predict 193 PD-related genes that are largely supported (49.7%) in the literature and are specific to the investigated PINK1 mutation. Enrichment analysis in Kyoto Encyclopedia of Genes and Genomes pathways highlights 10 PD-related molecular mechanisms perturbed during early PD development. Finally, investigating the top 20 prioritized genes reveals 12 previously unrecognized genes associated with PD that represent interesting drug targets.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.